Spark Structured Streaming

Spark Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. It allows for processing real-time data streams as well as batch data.

• **Streams**: Continuous data streams that can be processed in real-time.
• **Micro-batches**: The processing model where data is processed in small batches for efficiency.
• **Triggers**: Define how often the system will process the incoming data.
• **Output Modes**: How results are written to sinks, including Append, Complete, and Update.

The architecture of Spark Structured Streaming consists of several components, including:

• **Data Sources**: Where the streaming data originates (e.g., Kafka, files).
• **Streaming Query**: The defined processing logic that processes the data stream.
• **Output Sinks**: Where the processed data is sent (e.g., databases, dashboards).

To get started with Spark Structured Streaming, follow these steps:

1. Set up your Spark environment.
2. Create a Spark session:

from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("StructuredStreamingExample") \
    .getOrCreate()

3. Read streaming data:

df = spark.readStream \
    .format("kafka") \
    .option("kafka.bootstrap.servers", "localhost:9092") \
    .option("subscribe", "topic_name") \
    .load()

4. Define processing logic:

processed_df = df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")

5. Write to an output sink:

query = processed_df.writeStream \
    .outputMode("append") \
    .format("console") \
    .start()

6. Await termination:

query.awaitTermination()

Consider the following best practices when working with Spark Structured Streaming:

• Use appropriate checkpointing to ensure fault tolerance.
• Optimize your query by using DataFrames and SQL optimizations.
• Monitor performance and tune resources as needed.
• Use watermarking to handle late data.